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Delta-Gap Source Excitation Model in Surface-Volume-Surface Electric Field Integral Equation for 3-D Interconnect Characterization

2019· article· en· W3017702512 on OpenAlexaff
Ammar Aljamal, Reza Gholami, Shucheng Zheng, Vladimir Okhmatovski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsElectric-field integral equationPerfect conductorIntegral equationMethod of moments (probability theory)DiscretizationElectrical impedanceMathematical analysisExcitationPhysicsComputationComputational physicsMathematicsOpticsScatteringAlgorithm

Abstract

fetched live from OpenAlex

A delta-gap source model is proposed for Surface-Volume-Surface Electric Field Integral Equation (SVS-EFIE) which is a new class of single source integral equation (SSIE). The proposed excitation model enables the use of SVS-EFIE for full-wave analysis of interconnects which accurately accounts for the loss in the conductors and allows for rigorous handling of the substrate multilayered medium. The delta-gap source model of SVS-EFIE is derived based on the conventional model that was formulated for the classic surface Electric Field Integral Equation (EFIE). The computation of the network parameters, however, requires computation of the electric fields at the ports volumetric cross-sections. The SVS-EFIE and the proposed source model are discretized using Method of Moments (MoM). Preliminary numerical results are provided for current distribution and the input impedance calculations of a dipole antenna. The input impedance of the dipole antenna computed using proposed excitation model is shown to agree well with that computed using classical surface EFIE provided the latter uses appropriate surface impedance model to account for the conductor loss.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.239
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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